Simm, Dominic Alexander

[["dc.bibliographiccitation.artnumber","e0191924"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","PLOS ONE"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Simm, Dominic"],["dc.contributor.author","Kollmar, Martin"],["dc.contributor.editor","de Brevern, Alexandre G."],["dc.date.accessioned","2019-07-30T10:23:31Z"],["dc.date.available","2019-07-30T10:23:31Z"],["dc.date.issued","2018"],["dc.description.abstract","Stable single-alpha helices (SAH-domains) function as rigid connectors and constant force springs between structural domains, and can provide contact surfaces for protein-protein and protein-RNA interactions. SAH-domains mainly consist of charged amino acids and are monomeric and stable in polar solutions, characteristics which distinguish them from coiled-coil domains and intrinsically disordered regions. Although the number of reported SAH-domains is steadily increasing, genome-wide analyses of SAH-domains in eukaryotic genomes are still missing. Here, we present Waggawagga-CLI, a command-line tool for predicting and analysing SAH-domains in protein sequence datasets. Using Waggawagga-CLI we predicted SAH-domains in 24 datasets from eukaryotes across the tree of life. SAH-domains were predicted in 0.5 to 3.5% of the protein-coding content per species. SAH-domains are particularly present in longer proteins supporting their function as structural building block in multi-domain proteins. In human, SAH-domains are mainly used as alternative building blocks not being present in all transcripts of a gene. Gene ontology analysis showed that yeast proteins with SAH-domains are particular enriched in macromolecular complex subunit organization, cellular component biogenesis and RNA metabolic processes, and that they have a strong nuclear and ribonucleoprotein complex localization and function in ribosome and nucleic acid binding. Human proteins with SAH-domains have roles in all types of RNA processing and cytoskeleton organization, and are predicted to function in RNA binding, protein binding involved in cell and cell-cell adhesion, and cytoskeletal protein binding. Waggawagga-CLI allows the user to adjust the stabilizing and destabilizing contribution of amino acid interactions in i,i+3 and i,i+4 spacings, and provides extensive flexibility for user-designed analyses."],["dc.identifier.doi","10.1371/journal.pone.0191924"],["dc.identifier.pmid","29444145"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15675"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62193"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.eissn","1932-6203"],["dc.relation.issn","1932-6203"],["dc.relation.orgunit","Fakultät für Mathematik und Informatik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Waggawagga-CLI: A command-line tool for predicting stable single α-helices (SAH-domains), and the SAH-domain distribution across eukaryotes"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0174639"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Simm, Dominic"],["dc.contributor.author","Hatje, Klas"],["dc.contributor.author","Kollmar, Martin"],["dc.date.accessioned","2018-11-07T10:25:09Z"],["dc.date.available","2018-11-07T10:25:09Z"],["dc.date.issued","2017"],["dc.description.abstract","Stable single-alpha helices (SAHs) are versatile structural elements in many prokaryotic and eukaryotic proteins acting as semi-flexible linkers and constant force springs. This way SAH-domains function as part of the lever of many different myosins. Canonical myosin levers consist of one or several IQ-motifs to which light chains such as calmodulin bind. SAH-domains provide flexibility in length and stiffness to the myosin levers, and may be particularly suited for myosins working in crowded cellular environments. Although the function of the SAH-domains in human class-6 and class-10 myosins has well been characterised, the distribution of the SAH-domain in all myosin subfamilies and across the eukaryotic tree of life remained elusive. Here, we analysed the largest available myosin sequence dataset consisting of 7919 manually annotated myosin sequences from 938 species representing all major eukaryotic branches using the SAH-prediction algorithm of Waggawagga, a recently developed tool for the identification of SAH-domains. With this approach we identified SAH-domains in more than one third of the supposed 79 myosin subfamilies. Depending on the myosin class, the presence of SAH-domains can range from a few to almost all class members indicating complex patterns of independent and taxon-specific SAH-domain gain and loss."],["dc.identifier.doi","10.1371/journal.pone.0174639"],["dc.identifier.isi","000399351000027"],["dc.identifier.pmid","28369123"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14494"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/42794"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Distribution and evolution of stable single alpha-helices (SAH domains) in myosin motor proteins"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","1900066"],["dc.bibliographiccitation.firstpage","1900066"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","BioEssays"],["dc.bibliographiccitation.volume","41"],["dc.contributor.author","Hatje, Klas"],["dc.contributor.author","Mühlhausen, Stefanie"],["dc.contributor.author","Simm, Dominic"],["dc.contributor.author","Kollmar, Martin"],["dc.date.accessioned","2019-12-03T13:51:10Z"],["dc.date.accessioned","2021-10-27T13:18:29Z"],["dc.date.available","2019-12-03T13:51:10Z"],["dc.date.available","2021-10-27T13:18:29Z"],["dc.date.issued","2019"],["dc.description.abstract","The major transcript variants of human protein-coding genes are annotated to a certain degree of accuracy combining manual curation, transcript data, and proteomics evidence. However, there is considerable disagreement on the annotation of about 2000 genes-they can be protein-coding, noncoding, or pseudogenes-and on the annotation of most of the predicted alternative transcripts. Pure transcriptome mapping approaches seem to be limited in discriminating functional expression from noise. These limitations have partially been overcome by dedicated algorithms to detect alternative spliced micro-exons and wobble splice variants. Recently, knowledge about splice mechanism and protein structure are incorporated into an algorithm to predict neighboring homologous exons, often spliced in a mutually exclusive manner. Predicted exons are evaluated by transcript data, structural compatibility, and evolutionary conservation, revealing hundreds of novel coding exons and splice mechanism re-assignments. The emerging human pan-genome is necessitating distinctive annotations incorporating differences between individuals and between populations."],["dc.identifier.doi","10.1002/bies.201900066"],["dc.identifier.eissn","1521-1878"],["dc.identifier.issn","0265-9247"],["dc.identifier.pmid","31544971"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16825"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91876"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.eissn","1521-1878"],["dc.relation.issn","1521-1878"],["dc.relation.issn","0265-9247"],["dc.relation.orgunit","Fakultät für Mathematik und Informatik"],["dc.rights","CC BY 4.0"],["dc.rights.access","openAccess"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject","alternative splicing; human genome annotation; human pan-genome; micro-exon; mutually exclusive exons; protein-coding genes; wobble splicing"],["dc.subject.ddc","510"],["dc.title","The Protein‐Coding Human Genome: Annotating High‐Hanging Fruits"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","12439"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Simm, Dominic"],["dc.contributor.author","Hatje, Klas"],["dc.contributor.author","Waack, Stephan"],["dc.contributor.author","Kollmar, Martin"],["dc.date.accessioned","2021-10-01T09:57:44Z"],["dc.date.available","2021-10-01T09:57:44Z"],["dc.date.issued","2021"],["dc.description.abstract","Abstract Coiled-coil regions were among the first protein motifs described structurally and theoretically. The simplicity of the motif promises that coiled-coil regions can be detected with reasonable accuracy and precision in any protein sequence. Here, we re-evaluated the most commonly used coiled-coil prediction tools with respect to the most comprehensive reference data set available, the entire Protein Data Bank, down to each amino acid and its secondary structure. Apart from the 30-fold difference in minimum and maximum number of coiled coils predicted the tools strongly vary in where they predict coiled-coil regions. Accordingly, there is a high number of false predictions and missed, true coiled-coil regions. The evaluation of the binary classification metrics in comparison with naïve coin-flip models and the calculation of the Matthews correlation coefficient, the most reliable performance metric for imbalanced data sets, suggests that the tested tools’ performance is close to random. This implicates that the tools’ predictions have only limited informative value. Coiled-coil predictions are often used to interpret biochemical data and are part of in-silico functional genome annotation. Our results indicate that these predictions should be treated very cautiously and need to be supported and validated by experimental evidence."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.1038/s41598-021-91886-w"],["dc.identifier.pii","91886"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89905"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-469"],["dc.relation.eissn","2045-2322"],["dc.rights","CC BY 4.0"],["dc.title","Critical assessment of coiled-coil predictions based on protein structure data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]